Bibliographic record
Abstract
This book sets out to explore one of the core battlegrounds of Internet governance: the encryption of online communications. Current debates around encryption have fundamental implications for our individual liberties and collective presence on the Internet. Encryption of communications at scale and in increasingly usable ways has become a matter of public concern, especially since Edward Snowden’s 2013 revelations. A new cryptographic imaginary is taking hold, which sees encryption as a necessary precondition for the formation of networked publics. At the same time, there have been major evolutions and accelerations in the field of secure communications, prompted in part by the cryptography community’s renewed efforts to create next-generation secure messaging protocols and applications. It is vital that we unveil the very recent, and sometimes less recent history of these protocols and their key applications. The book takes on this task, in order to show how the opportunities and constraints they provide to Internet users came about, and how both developer communities and institutions are working towards making them available for the largest possible audience. It explores how efforts towards this goal are built upon interwoven stories about technical development and architectural choices, about community-building — and about Internet governance and politics. In doing so, the book focuses on the experience of encryption in a wide variety of contemporary secure messaging protocols and tools, and looks at the implications of these endeavors for the “making of” digital liberties on the Internet. Concealing for Freedom provides two key empirical and theoretical contributions. Firstly, it enriches a social sciences-informed understanding of encryption. It does so by examining how different solutions of cryptography for secure communications are created, developed, enacted and governed, and what this diverse experience of encryption, operating across many different sites, means for online civil liberties. Secondly, it contributes to understanding the social and political implications of particular design choices when it comes to the technical architecture of digital networks, in particular their degree of (de-)centralization. The book explores developers’ actions and their interactions with other stakeholders, for instance users, security trainers, standardising bodies, and funding organizations. It also examines their interactions with the technical artifacts they develop, in which a core common objective is to create tools that “conceal for freedom” even as how this objective is met differs according to technical architectures, the user publics being targeted and the tools’ underlying values and business models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".